In a mobile communication system, antenna diversity can be obtained by multiple antennas provided in a mobile station and a base station transmission apparatus and thus reliability of signals transmitted between the base station transmitting apparatus and the mobile station can be increased.
In a wireless base station that transmits/receives signals by using a plurality of antennas, a smart antenna obtains an antenna gain by multiplying weight vectors by signals transmitted/received through a plurality of antennas and moderates co-channel interference at the same time, thereby improving system performance, wherein the plurality of antennas are spaced by half-wavelengths from each other and form a linear array antenna.
The smart antenna algorithm is divided into a switching-beam algorithm and an adaptive-beam algorithm depending on a weighting vector decision method.
The switching beam algorithm prepares a set of predefined weighting vectors and selects an appropriate weighting vector from among them depending on circumstances. Such a switching beam algorithm prepares and stores the weighting vector set in advance, and accordingly necessitates an increase in capacity of hardware and software. In addition, it is difficult to calculate an optimal weighting vector since the most appropriate weighting vector for the corresponding condition is selected from the predefined weighting vector set.
The adaptive-beam forming algorithm selects the most appropriate weighting vector by using various algorithms. A smart antenna algorithm using such an adaptive-beam forming algorithm selects a weighting vector by using sample matrix inversion (SMI), least mean square (LMS), recursive least square (RLS), constant modulus algorithm (CMA), conjugate gradient, and neural network approaches.
However, a weighting vector selection method of the smart antenna algorithm using such an adaptive-beam forming algorithm requires a training signal for selecting a weighting vector, and therefore it is difficult to apply this method to a packet signal that contains a small amount of data. In addition, the weighting vector selection method performs too many computations, requiring complex hardware and software configurations.
The above information disclosed in this Background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art.